import cv2 import json import numpy as np

img_path = "D:/dummy pic.jpeg" i=cv2.imread(img_path) i=cv2.resize(i,(300,300)) g = cv2.cvtColor(i, cv2.COLOR_BGR2GRAY) n = cv2.bitwise_not(i) def adjust_brightness(image, value): hsv = cv2.cvtColor(image, cv2.COLOR_BGR2HSV) h, s, v = cv2.split(hsv) v = cv2.add(v, value) final_hsv = cv2.merge((h, s, v)) brightened_image = cv2.cvtColor(final_hsv, cv2.COLOR_HSV2BGR) return brightened_image def adjust_saturation(image, value): # Convert the image to HSV (Hue, Saturation, Value) hsv = cv2.cvtColor(image, cv2.COLOR_BGR2HSV) h, s, v = cv2.split(hsv)

# Convert the S channel to a float32 type for accurate multiplication
s = s.astype(np.float32)

# Multiply the S channel by the value (increase or decrease saturation)
s = s * (1 + value / 100.0)

# Clip the values to stay within 0-255 range and convert back to uint8
s = np.clip(s, 0, 255).astype(np.uint8)

# Combine the modified channels back
final_hsv = cv2.merge((h, s, v))

# Convert back to BGR color space
saturated_image = cv2.cvtColor(final_hsv, cv2.COLOR_HSV2BGR)

return saturated_image

def unsharp_mask(image, kernel_size=(5, 5), sigma=1.0, amount=1.5, threshold=0): # Apply Gaussian blur blurred = cv2.GaussianBlur(image, kernel_size, sigma)

# Calculate the unsharp mask
sharpened = float(amount + 1) * image - float(amount) * blurred

# Clip values to stay within valid range
sharpened = np.clip(sharpened, 0, 255).astype(np.uint8)

# Apply thresholding to control the level of sharpening
if threshold > 0:
    low_contrast_mask = np.absolute(image - blurred) < threshold
    np.copyto(sharpened, image, where=low_contrast_mask)

return sharpened

i1=adjust_brightness(i,50) i2=adjust_saturation(i,80) i3=g i4=unsharp_mask(i) cv2.imwrite("./editedByBrightness.png",i1) cv2.imwrite("./original.png",i) cv2.imwrite("./editedBySaturation.png",i2) cv2.imwrite("./greyscale.png",i3) cv2.imwrite("./distinct.png",i4) cv2.imwrite("./negetive.png",n) laplacian = cv2.Laplacian(g, cv2.CV_64F) laplacian = np.uint8(np.absolute(laplacian)) cv2.imwrite("./edgeByLaplacian.png",laplacian) blurred_image = cv2.GaussianBlur(g, (5, 5), 1.4) canny = cv2.Canny(blurred_image, threshold1=50, threshold2=150) cv2.imwrite("./edgeByCanny.png",canny) g4 = cv2.cvtColor(i4, cv2.COLOR_BGR2GRAY) cv2.imwrite("./greyscaleDistinct.png",g4) pic = i text = 'Dogs' org = (80, 50) # Bottom-left corner of the text string in the image font = cv2.FONT_HERSHEY_SIMPLEX # Font type font_scale = 1 # Font scale color = (101, 0, 255) # Text color in BGR (blue, green, red) thickness = 2 # Thickness of the lines used to draw the text

cv2.putText(pic, text, org, font, font_scale, color, thickness) cv2.imwrite("./imageWithText.png",pic)

hsv_image = cv2.cvtColor(i, cv2.COLOR_BGR2HSV) hue_adjustment = 100 # Value to adjust the hue hsv_image[:, :, 0] = (hsv_image[:, :, 0] + hue_adjustment) % 180 ih = cv2.cvtColor(hsv_image, cv2.COLOR_HSV2BGR) cv2.imwrite("./imageHue.png", ih) #i1=cv2.resize(i1,(128,128)) #i2=adjust_brightness(i,-50) #i2=cv2.resize(i2,(128,128)) #cv2.imshow("original",i) #cv2.imshow("edited",i1) #cv2.waitKey(0) #cv2.destroyAllWindows() #g = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)

face_cascade = cv2.CascadeClassifier(cv2.data.haarcascades + 'haarcascade_frontalface_default.xml')

faces = face_cascade.detectMultiScale(g, scaleFactor=1.1, minNeighbors=5, minSize=(30, 30))

for (x, y, w, h) in faces: cv2.rectangle(i, (x, y), (x+w, y+h), (255, 0, 0), 2)

cv2.imwrite('./Detected Faces.png', i)